Soft Sensor and Adaptive Model-Based Dissolved Oxygen Control for Biological Wastewater Treatment Processes

Abstract

In wastewater treatment processes (WWTP), the respiration rate (R) and oxygen transfer rate (K_La) are two of the most important variables for monitoring biological activity and assessing process control performance. Knowledge of the two variables is therefore of interest in both process diagnosis and process control. In consideration of the combined mechanism of dissolved oxygen (DO) control and R/K_La estimation, we used a generalized damped least squares (GDLS) method as a soft sensor of the two key variables under closed loop control. The estimated values of the two key variables were then used to derive an adaptive model-based DO control law using the soft sensing ability of the GDLS. Simulation results show that the GDLS algorithm gives excellent estimations of the respiration rate under closed loop control, and that model-based DO control can efficiently deal with changes in the operating condition.